We are graduate students and postdocs working on basic research in the neurosciences at Harvard University. We are excited about neuroscience and hope to convince you - whether you’ve never heard of brains or are a seasoned scientist - that brain research is one of the most fascinating areas of science today.

Just a 3-pound greyish mass, the brain may not look too intimidating. Yet scientists have puzzled for decades over how to dissect the tight network of the brain’s 100 billion connected neurons that at first all seem alike. Over the last century, the field of neuroscience has developed to investigate this intriguing subject, and within it different approaches have grown into distinct disciplines. These all similarly aim to unravel the networks underlying animal behavior, but do so using different techniques and methods.

For example, one discipline that has been quickly expanding and receiving much attention from the media is connectomics. The aim of this field is to create detailed maps, or connectomes, of all synaptic connections among neurons. This stands in contrast to other more conventional approaches that interrogate specific pathways and networks of connections among a restricted group of neurons. The generation of these connectomes has been strongly pursued through the development of techniques for high-resolution, high-throughput reconstruction of small volumes of nervous tissue. For example, recent work by Kasthuri et al (2015) shows the potential of new electron microscopy (EM) techniques for expanding our understanding of the connections and structure of the brain. Unlike other forms of histology that rely on fluorescence to visualize cells, EM generates high-resolution images that reveal all membranous structures. Kasthuri and colleagues have imaged a square volume of brain slice after brain slice, and have then combined the slices and traced the cells across them, thereby reconstructing their full shape.

While connectomics is an exciting new approach to studying brain circuits, here I want to briefly discuss recent advances in another discipline of neuroscience. In order to make sense of the billions of neurons in the brain, scientists are classifying neurons into distinct types and subtypes. Knowing the identity of the different kinds of ‘players’ in the brain can be critically important to understanding the game, and a tool to interrogate the formation and function of networks.

The classification of neurons is generally based on genetic or proteic similarity. While it is very rare for one gene to alone give identity to a neuron, combinatorial expression patterns can often be used as markers to identify cells. This is a sort of bar code for each neuron, and scanning it would allow scientists to itemize the cell with others of its kind.

Most work on neuronal type classification has been done using antibodies (especially against transcription factors). For instance, there are extensive libraries of markers for cells in the different cortical layers. Recent work by Macosko et al (2015) at the Harvard Medical School has introduced a method for a more comprehensive classification of neurons. They used DropSeq, a new technique that allows one to read out sequences of RNA in single cells by trapping and rupturing the cells in nanodroplets, taking out all mRNA strands, and assigning to them a cell-specific bar-code. Each mRNA strand could then be sequenced and assigned to every cell it is expressed in. Knowing the complete gene expression profile of each cell allows comparison to other cells and clustering them by similarity!

Macosko and colleagues tested this new method on the retina. Here, the ingredients for the circuit have actually already been known for quite some time. For instance, different classes of interneurons (i.e. bipolar cells) synapse specifically onto separate types of projection neurons (i.e. retinal ganglion cells). Many of these types have previously been identified, and were matched to the clusters of cells found with DropSeq.

As we scramble to find the elaborate and specific genetic bar-codes that identify cell groups, we often aren’t even beginning to think of the role of these characteristic gene expression patterns in shaping the cell. Nevertheless genetic bar-codes have been useful because they allow to manipulate networks, and thereby provide a powerful strategy to probe their functions. Genetic signatures for cell groups have become a central tool in neuroscience. Ever-growing are the libraries of transgenic lines that allow for specific fluorescent protein expressions, as well as genetic manipulation (e.g. knock-outs) in select groups of neurons.

A huge concern when using these tools is the possibility that the markers are not reliable or specific enough to really study precise families or groups of neurons. In a comprehensive review of viral and transgenic reporters for adult-born neurons, Enikolopov et al (2015) admit to variability arising from the use of single gene promoters to drive expression of fluorescent proteins in separate cell type populations. For instance, some transgenic lines may not label all cells of a type, while others include cells that are morphologically and functionally dissimilar. Variations in timing and intensity of gene expression are only two of the possible explanations for this ‘leakiness’ in transgenic lines. While conceptually appealing, cell types aren’t as clearly defined as we’d like them to be. It is a slippery slope from here to saying that all cells are unique, and that it is their placement and role in a network that defines their function, not their genetic identity. And yet, gene expression does define a cell’s migration pattern, its morphological growth, and its excitability properties. Cell types may not be fully distinct, but genetically classifying neurons remains an incredible resource for understanding the structural and functional role of single cells in neural pathways.